Searched defs:jacobian (Results 1 - 25 of 33) sorted by relevance

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/external/ceres-solver/internal/ceres/
H A Ddynamic_compressed_row_finalizer.h42 DynamicCompressedRowSparseMatrix* jacobian = local
44 jacobian->Finalize(num_parameters);
H A Devaluator_test_utils.h44 const double jacobian[200]; member in struct:ceres::internal::ExpectedEvaluation
H A Dblock_evaluate_preparer.cc49 // Point the jacobian blocks directly into the block sparse matrix.
52 SparseMatrix* jacobian,
54 // If the overall jacobian is not available, use the scratch space.
55 if (jacobian == NULL) {
58 jacobian,
64 down_cast<BlockSparseMatrix*>(jacobian)->mutable_values();
50 Prepare(const ResidualBlock* residual_block, int residual_block_index, SparseMatrix* jacobian, double** jacobians) argument
H A Dcorrector.cc46 // and the jacobian are scaled by the squareroot of the derivative
121 double* jacobian) {
123 DCHECK(jacobian != NULL);
127 VectorRef(jacobian, num_rows * num_cols) *= sqrt_rho1_;
146 r_transpose_j += jacobian[r * num_cols + c] * residuals[r];
150 jacobian[r * num_cols + c] = sqrt_rho1_ *
151 (jacobian[r * num_cols + c] -
118 CorrectJacobian(const int num_rows, const int num_cols, double* residuals, double* jacobian) argument
H A Dcorrector_test.cc60 double jacobian = 10.0; local
74 // The jacobian in this case will be
75 // sqrt(kRho[1]) * (1 - kAlpha) * jacobian.
76 const double kExpectedJacobian = sqrt(kRho[1]) * (1 - kAlpha) * jacobian;
79 c.CorrectJacobian(1.0, 1.0, &residuals, &jacobian);
83 ASSERT_NEAR(kExpectedJacobian, jacobian, 1e-6);
88 double jacobian = 10.0; local
102 // The jacobian in this case will be
103 // sqrt(kRho[1]) * jacobian.
104 const double kExpectedJacobian = sqrt(kRho[1]) * jacobian;
116 double jacobian = 10.0; local
147 double jacobian[2 * 3]; local
215 double jacobian[2 * 3]; local
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H A Dnormal_prior_test.cc70 double * jacobian = new double[num_rows * num_cols]; local
74 prior.Evaluate(&x, residuals.data(), &jacobian);
82 MatrixRef J(jacobian, num_rows, num_cols);
87 delete []jacobian;
H A Ddynamic_compressed_row_jacobian_writer.cc48 // Initialize `jacobian` with zero number of `max_num_nonzeros`.
52 DynamicCompressedRowSparseMatrix* jacobian = local
58 program_, jacobian);
60 return jacobian;
67 DynamicCompressedRowSparseMatrix* jacobian = local
79 // `residual_offset` is the residual row in the global jacobian.
80 // Empty the jacobian rows.
81 jacobian->ClearRows(residual_offset, num_residuals);
98 jacobian->InsertEntry(
H A Devaluator.h82 // The residual, gradients and jacobian pointers can be NULL, in
100 CRSMatrix* jacobian);
103 // of the objective function. The jacobian has dimensions
115 // the jacobian for use with CHOLMOD, where as BlockOptimizationProblem
116 // creates a BlockSparseMatrix representation of the jacobian for use in the
133 // residuals, and jacobian in the corresponding arguments. Both residuals and
134 // jacobian are optional; to avoid computing them, pass NULL.
137 // values array of the jacobian is modified.
146 SparseMatrix* jacobian) = 0;
155 SparseMatrix* jacobian) {
151 Evaluate(const double* state, double* cost, double* residuals, double* gradient, SparseMatrix* jacobian) argument
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H A Dlevenberg_marquardt_strategy.cc67 SparseMatrix* jacobian,
70 CHECK_NOTNULL(jacobian);
74 const int num_parameters = jacobian->num_cols();
80 jacobian->SquaredColumnNorm(diagonal_.data());
104 // Then x can be found as x = -y, but the inputs jacobian and residuals
107 linear_solver_->Solve(jacobian, residuals, solve_options, step);
125 jacobian,
65 ComputeStep( const TrustRegionStrategy::PerSolveOptions& per_solve_options, SparseMatrix* jacobian, const double* residuals, double* step) argument
H A Dautodiff_local_parameterization_test.cc64 double jacobian[9]; local
65 parameterization.ComputeJacobian(x, jacobian);
69 EXPECT_EQ(jacobian[k], (i == j) ? 1.0 : 0.0);
105 double jacobian[9]; local
106 parameterization.ComputeJacobian(x, jacobian);
110 EXPECT_NEAR(jacobian[k], (i == j) ? 1.2345 : 0.0, kTolerance);
157 double jacobian[12]; local
160 parameterization.ComputeJacobian(x, jacobian);
175 EXPECT_TRUE(IsFinite(jacobian[i]));
176 EXPECT_NEAR(jacobian[
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H A Ddense_jacobian_writer.h31 // A jacobian writer that writes to dense Eigen matrices.
58 // them over to the larger jacobian later.
72 SparseMatrix* jacobian) {
74 if (jacobian != NULL) {
75 dense_jacobian = down_cast<DenseSparseMatrix*>(jacobian);
82 // Now copy the jacobians for each parameter into the dense jacobian matrix.
69 Write(int residual_id, int residual_offset, double **jacobians, SparseMatrix* jacobian) argument
H A Ddogleg_strategy_test.cc77 Matrix jacobian = sqrtD * basis; local
78 jacobian_.reset(new DenseSparseMatrix(jacobian));
82 residual_ = -jacobian * minimum;
105 Matrix jacobian = Ddiag.asDiagonal(); local
106 jacobian_.reset(new DenseSparseMatrix(jacobian));
110 residual_ = -jacobian * minimum;
H A Dresidual_block_utils_test.cc62 double jacobian; local
63 double* jacobians[] = { &jacobian };
H A Dblock_jacobian_writer.cc47 // per-parameter jacobian goes where in the overall program jacobian.
63 // are there. This will determine where the F blocks start in the jacobian
64 // matrix. Also compute the number of jacobian blocks.
135 // Create evaluate prepareres that point directly into the final jacobian. This
206 BlockSparseMatrix* jacobian = new BlockSparseMatrix(bs); local
207 CHECK_NOTNULL(jacobian);
208 return jacobian;
H A Dcompressed_row_jacobian_writer.cc44 const Program* program, CompressedRowSparseMatrix* jacobian) {
47 vector<int>& col_blocks = *(jacobian->mutable_col_blocks());
55 vector<int>& row_blocks = *(jacobian->mutable_row_blocks());
88 // Count the number of jacobian nonzeros.
102 // Allocate storage for the jacobian with some extra space at the end.
103 // Allocate more space than needed to store the jacobian so that when the LM
106 CompressedRowSparseMatrix* jacobian = local
114 int* rows = jacobian->mutable_rows();
115 int* cols = jacobian->mutable_cols();
150 // parameter vector. This code mirrors that in Write(), where jacobian
43 PopulateJacobianRowAndColumnBlockVectors( const Program* program, CompressedRowSparseMatrix* jacobian) argument
182 CompressedRowSparseMatrix* jacobian = local
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H A Ddynamic_numeric_diff_cost_function_test.cc120 // Prepare the jacobian.
124 vector<double*> jacobian; local
125 jacobian.push_back(jacobian_vect[0].data());
126 jacobian.push_back(jacobian_vect[1].data());
128 // Test jacobian computation.
131 jacobian.data()));
187 // Prepare the jacobian.
191 vector<double*> jacobian; local
192 jacobian.push_back(NULL);
193 jacobian
241 vector<double*> jacobian; local
444 vector<double*> jacobian; local
474 vector<double*> jacobian; local
496 vector<double*> jacobian; local
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H A Dlocal_parameterization_test.cc55 double jacobian[9]; local
56 parameterization.ComputeJacobian(x, jacobian);
60 EXPECT_EQ(jacobian[k], (i == j) ? 1.0 : 0.0);
106 double jacobian[4 * 3]; local
107 parameterization.ComputeJacobian(x, jacobian);
113 EXPECT_EQ(jacobian[jacobian_cursor], delta_cursor == k ? 1.0 : 0.0);
118 EXPECT_EQ(jacobian[jacobian_cursor], 0.0);
184 // Autodiff jacobian at delta_x = 0.
188 double jacobian[12]; local
189 param.ComputeJacobian(x, jacobian);
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H A Ddogleg_strategy.cc79 SparseMatrix* jacobian,
82 CHECK_NOTNULL(jacobian);
86 const int n = jacobian->num_cols();
121 jacobian->SquaredColumnNorm(diagonal_.data());
127 ComputeGradient(jacobian, residuals);
128 ComputeCauchyPoint(jacobian);
131 ComputeGaussNewtonStep(per_solve_options, jacobian, residuals);
152 if (!ComputeSubspaceModel(jacobian)) {
172 SparseMatrix* jacobian,
175 jacobian
77 ComputeStep( const TrustRegionStrategy::PerSolveOptions& per_solve_options, SparseMatrix* jacobian, const double* residuals, double* step) argument
171 ComputeGradient( SparseMatrix* jacobian, const double* residuals) argument
181 ComputeCauchyPoint(SparseMatrix* jacobian) argument
514 ComputeGaussNewtonStep( const PerSolveOptions& per_solve_options, SparseMatrix* jacobian, const double* residuals) argument
645 ComputeSubspaceModel(SparseMatrix* jacobian) argument
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H A Ddynamic_autodiff_cost_function_test.cc119 // Prepare the jacobian.
123 vector<double*> jacobian; local
124 jacobian.push_back(jacobian_vect[0].data());
125 jacobian.push_back(jacobian_vect[1].data());
127 // Test jacobian computation.
130 jacobian.data()));
186 // Prepare the jacobian.
190 vector<double*> jacobian; local
191 jacobian.push_back(NULL);
192 jacobian
240 vector<double*> jacobian; local
443 vector<double*> jacobian; local
473 vector<double*> jacobian; local
495 vector<double*> jacobian; local
687 vector<double*> jacobian; local
713 vector<double*> jacobian; local
744 vector<double*> jacobian; local
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H A Devaluator_test.cc70 // evaluator into the "local" jacobian. In the tests, the "subset
72 // from these jacobians. Put values in the jacobian that make this
82 MatrixRef jacobian(jacobians[k],
86 jacobian.col(j).setConstant(kFactor * (j + 1));
155 scoped_ptr<SparseMatrix> jacobian(evaluator->CreateJacobian());
159 ASSERT_EQ(expected_num_rows, jacobian->num_rows());
160 ASSERT_EQ(expected_num_cols, jacobian->num_cols());
169 expected_jacobian != NULL ? jacobian.get() : NULL));
173 jacobian->ToDenseMatrix(&actual_jacobian);
197 (i & 4) ? expected.jacobian
578 double* jacobian = jacobians[0]; local
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H A Dminimizer.h113 jacobian = NULL;
180 SparseMatrix* jacobian; member in struct:ceres::internal::Minimizer::Options
H A Dproblem.cc202 CRSMatrix* jacobian) {
207 jacobian);
198 Evaluate(const EvaluateOptions& evaluate_options, double* cost, vector<double>* residuals, vector<double>* gradient, CRSMatrix* jacobian) argument
H A Dprogram_evaluator.h32 // and stores the result into a jacobian. The particular type of jacobian is
36 // pointers to the jacobian blocks where the cost function evaluates to.
38 // jacobian blocks in the passed sparse matrix.
55 // SparseMatrix* jacobian,
60 // // Create a jacobian that this writer can write. Same as
69 // // larger sparse jacobian.
73 // SparseMatrix* jacobian);
104 void operator()(SparseMatrix* jacobian, int num_parameters) {} argument
139 SparseMatrix* jacobian) {
134 Evaluate(const Evaluator::EvaluateOptions& evaluate_options, const double* state, double* cost, double* residuals, double* gradient, SparseMatrix* jacobian) argument
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/external/ceres-solver/include/ceres/internal/
H A Dnumeric_diff.h95 double *jacobian) {
114 Map<JacobianMatrix> parameter_jacobian(jacobian,
148 // Compute this column of the jacobian in 3 steps:
196 double *jacobian) {
89 EvaluateJacobianForParameterBlock( const CostFunctor* functor, double const* residuals_at_eval_point, const double relative_step_size, int num_residuals, double **parameters, double *jacobian) argument
190 EvaluateJacobianForParameterBlock( const CostFunctor* functor, double const* residuals_at_eval_point, const double relative_step_size, const int num_residuals, double **parameters, double *jacobian) argument
/external/eigen/unsupported/Eigen/src/AutoDiff/
H A DAutoDiffVector.h73 : m_values(other.values()), m_jacobian(other.jacobian())
77 : m_values(other.values()), m_jacobian(other.jacobian())
84 m_jacobian = other.jacobian();
91 m_jacobian = other.jacobian();
98 inline const JacobianType& jacobian() const { return m_jacobian; } function in class:Eigen::AutoDiffVector
99 inline JacobianType& jacobian() { return m_jacobian; } function in class:Eigen::AutoDiffVector
111 m_jacobian + other.jacobian());
119 m_jacobian += other.jacobian();
133 m_jacobian - other.jacobian());
141 m_jacobian -= other.jacobian();
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